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Maximizing and evaluating the impact of test-trace-isolate programs: A modeling study.
Grantz, Kyra H; Lee, Elizabeth C; D'Agostino McGowan, Lucy; Lee, Kyu Han; Metcalf, C Jessica E; Gurley, Emily S; Lessler, Justin.
  • Grantz KH; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
  • Lee EC; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
  • D'Agostino McGowan L; Department of Mathematics and Statistics, Wake Forest University, Winston-Salem, North Carolina, United States of America.
  • Lee KH; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
  • Metcalf CJE; Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey, United States of America.
  • Gurley ES; Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, United States of America.
  • Lessler J; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America.
PLoS Med ; 18(4): e1003585, 2021 04.
Article in English | MEDLINE | ID: covidwho-1209521
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ABSTRACT

BACKGROUND:

Test-trace-isolate programs are an essential part of coronavirus disease 2019 (COVID-19) control that offer a more targeted approach than many other nonpharmaceutical interventions. Effective use of such programs requires methods to estimate their current and anticipated impact. METHODS AND

FINDINGS:

We present a mathematical modeling framework to evaluate the expected reductions in the reproductive number, R, from test-trace-isolate programs. This framework is implemented in a publicly available R package and an online application. We evaluated the effects of completeness in case detection and contact tracing and speed of isolation and quarantine using parameters consistent with COVID-19 transmission (R0 2.5, generation time 6.5 days). We show that R is most sensitive to changes in the proportion of cases detected in almost all scenarios, and other metrics have a reduced impact when case detection levels are low (<30%). Although test-trace-isolate programs can contribute substantially to reducing R, exceptional performance across all metrics is needed to bring R below one through test-trace-isolate alone, highlighting the need for comprehensive control strategies. Results from this model also indicate that metrics used to evaluate performance of test-trace-isolate, such as the proportion of identified infections among traced contacts, may be misleading. While estimates of the impact of test-trace-isolate are sensitive to assumptions about COVID-19 natural history and adherence to isolation and quarantine, our qualitative findings are robust across numerous sensitivity analyses.

CONCLUSIONS:

Effective test-trace-isolate programs first need to be strong in the "test" component, as case detection underlies all other program activities. Even moderately effective test-trace-isolate programs are an important tool for controlling the COVID-19 pandemic and can alleviate the need for more restrictive social distancing measures.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Contact Tracing / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: PLoS Med Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pmed.1003585

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Contact Tracing / COVID-19 / Models, Theoretical Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study / Qualitative research Limits: Humans Language: English Journal: PLoS Med Journal subject: Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pmed.1003585